Payment Card Fraud Identification
Rajnish Kumar1, Praneet Kumar Gaurav2, Swati Shahi3, Amol Sitaram Kardel4
1Rajnish Kumar, Department of Computer Engineering, Sir Visvesvaraya Institute of Technology, Nasik, India.
2Praneet Kumar Gaurav, Department of Computer Engineering, Sir Visvesvaraya Institute of Technology, Nasik India.
3Swati Shahi, Department of Computer Engineering, Sir Visvesvaraya Institute of Technology, Nasik, India.
4Amol Sitaram Kardel, Department of Computer Engineering, Sir Visvesvaraya Institute of Technology, Nasik, India.
Manuscript received on March 02, 2013. | Revised Manuscript received on April 13, 2013. | Manuscript published on April 30, 2013. | PP: 675-679 | Volume-2, Issue-4, April 2013. | Retrieval Number: D1587042413/2013©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper introduces the defensive methods and procedures to identify the payment card fraud. Payment card means credit/debit card which is used for payment purpose over internet. With the rapid advancement in internet, almost all the transaction are being offered by internet as online such as railway ticket booking, mobile recharge, paying the electricity or telephone bill, shopping and etc. this is very good thing because we save our time, we have multi option while shopping but when we transact over internet then chances of fraud also exists. In existing system, we know the fraud happened only when the transaction has been occurred. Sometimes, it become very difficult to identify the fraudulent and hence regarding loses occurs. In this article, we proposed a model namely Advanced Hidden Markov Model which will identify the fraud during transaction. This model is different from Hidden Markov Model. In this Advanced Hidden Markov Model, We used some other set of finite states which is linked through probability distribution states and not visible to user. This model first detect whether it is fraudulent or not and after then it process further so chances of fraud can be minimized using advanced hidden markov model.
Keywords: PC, AHMM, FIS, FP, PCFI.